Log file analysis for disengagement detection in e-Learning environments

Abstract

Most e-Learning systems store data about the learner's actions in log files, which give us detailed information about learner behaviour. Data mining and machine learning techniques can give meaning to these data and provide valuable information for learning improvement. One area that is of particular importance in the design of e-Learning systems is learner motivation as it is a key factor in the quality of learning and in the prevention of attrition. One aspect of motivation is engagement, a necessary condition for effective learning. Using data mining techniques for log file analysis, our research investigates the possibility of predicting users' level of engagement, with a focus on disengaged learners. As demonstrated previously across two different e-Learning systems, HTML-Tutor and iHelp, disengagement can be predicted by monitoring the learners' actions (e.g. reading pages and taking test/quizzes). In this paper we present the findings of three studies that refine this prediction approach. Results from the first study show that two additional reading speed attributes can increase the accuracy of prediction. The second study suggests that distinguishing between two different patterns of disengagement (spending a long time on a page/test and browsing quickly through pages/tests) may improve prediction in some cases. The third study demonstrates the influence of exploratory behaviour on prediction, as most users at the first login familiarize themselves with the system before starting to learn.

Assessment of Learners’ Motivation during Interactions with Serious Games: A Study of Some Motivational Strategies in Food-Force

“…For example, Schunk et al [5] used Keller's ARCS model (see next section for a description of this model) and proposed several rules to infer motivational states from two sources: the interactions of the students with the tutoring system and their motivational traits. Some researchers have analyzed log files and have established correlations between learners' actions in log files Advances in Human-Computer Interaction 3 and their motivational states (e.g., [24]). Other researchers have used physiological sensors to assess learners' motivation and correlate physiological learners' responses to some dimensions of motivation such as attention and confidence (e.g., [17,25] [19] has used biometric sensors (HR, SC, EMG, and RESP) and facial expression analysis to develop a probabilistic model of detecting students' affective states within an educational game.…”

Individual Differences Related to College Students’ Course Performance in Calculus II

“…In contrast, the real-time nature of LMS data may provide early warning signs of potentially at-risk students, enabling the implementation of intervention efforts before failure becomes inevitable (Milne, Jeffrey, Suddaby, & Higgins, 2012; Gašević et al, 2015). By moving away from summative assessments such as exams, and providing visualizable, real-time information about individual aspects of student engagement and learning (Cocea & Weibelzahl, 2009), the use of LMS data, also called "academic analytics" (Goldstein & Katz, 2005;Macfadyen & Dawson, 2010), allows educators to monitor individual academic progress step-by-step (Bienkowski, Feng & Means, 2012).With this study, we sought to determine the best individual differences predictors of student performance in a flipped Calculus II course. This course required students to interact with an LMS outside of class by watching videos of course content lectures and doing workshops and quizzes, as well as come to class for guided problem solving.…”

Identifying Learner’s Engagement in Learning Games - A Qualitative Approach based on Learner’s Traces of Interaction

“…For that purpose they collect preference data such as the game mode selected and behavioural data like average yards gained or ratio of possession. Dealing with learner's disengagement detection in web-based e-learning system, (Cocea and Weibelzahl, 2009) compare eight machine learning techniques on several raw data. The latter are mainly related to reading pages (number of pages read, time spent reading pages) and quizzes events.…”